Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
Reinforcement fine-tuning (RFT) has become a core paradigm for post-training large language models, yet its training process remains highly fragile. Existing efforts mainly improve reliability at the system level or address specific issues in individual subproblems by modifying RFT algorithms. Despite their effectiveness, they largely overlook the problem of failure management at the training-process level. When training goes wrong, practitioners still rely heavily on expert-driven manual inspection and correction, and automatic failure management for RFT remains largely unexplored. In this paper, we take a first step toward systematic failure management for reinforcement fine-tuning. To ...
| Authors | Lingzhe Zhang, Tong Jia, Yunpeng Zhai et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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